title: Scalable high-throughput identification of genetic targets by network filtering.
abstract: BACKGROUND
  Discovering the molecular targets of compounds or the cause of physiological conditions, among the multitude of known genes, is one of the major challenges of bioinformatics. One of the most common approaches to this problem is finding sets of differentially expressed, and more recently differentially co-expressed, genes. Other approaches require libraries of genetic mutants or require to perform a large number of assays. Another elegant approach is the filtering of mRNA expression profiles using reverse-engineered gene network models of the target cell. This approach has the advantage of not needing control samples, libraries or numerous assays. Nevertheless, the impementations of this strategy proposed so far are computationally demanding. Moreover the user has to arbitrarily choose a threshold on the number of potentially relevant genes from the algorithm output.
  
  
  RESULTS
  Our solution, while performing comparably to state of the art algorithms in terms of discovered targets, is more efficient in terms of memory and time consumption. The proposed algorithm computes the likelihood associated to each gene and outputs to the user only the list of likely perturbed genes.
  
  
  CONCLUSIONS
  The proposed algorithm is a valid alternative to existing algorithms and is particularly suited to contemporary gene expression microarrays, given the number of probe sets in each chip, also when executed on common desktop computers.
authors.affiliation: Department of Electrical and Electronics, Polytechnic of Bari, Via E. Orabona, 4, 70125 Bari, Italy. bevilacqua@poliba.it
authors.forename: Vitoantonio
authors.initials: V
authors.lastname: Bevilacqua
authors.forename: Paolo
authors.initials: P
authors.lastname: Pannarale
mesh_terms: D000465:Algorithms; D015870:Gene Expression / Q000187:drug effects; D020869:Gene Expression Profiling / Q000379:methods*; D058990:Molecular Targeted Therapy; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software